DNNSIM: Single-Index Neural Network for Skewed Heavy-Tailed Data

Provides a deep neural network model with a monotonic increasing single index function tailored for periodontal disease studies. The residuals are assumed to follow a skewed T distribution, a skewed normal distribution, or a normal distribution. More details can be found at Liu, Huang, and Bai (2024) <doi:10.1016/j.csda.2024.108012>.

Version:	0.1.1
Imports:	reticulate (≥ 1.37.0), stats (≥ 4.3.0), Rdpack (≥ 2.6)
Published:	2025-01-07
DOI:	10.32614/CRAN.package.DNNSIM
Author:	Qingyang Liu [aut, cre], Shijie Wang [aut], Ray Bai [aut], Dipankar Bandyopadhyay [aut]
Maintainer:	Qingyang Liu <rh8liuqy at gmail.com>
License:	GPL (≥ 3)
NeedsCompilation:	no
SystemRequirements:	Python (>= 3.8.0); PyTorch (https://pytorch.org/); NumPy (https://numpy.org/); SciPy (https://scipy.org/); sklearn (https://scikit-learn.org/stable/);
Materials:	NEWS
CRAN checks:	DNNSIM results

Documentation:

Reference manual:

DNNSIM.pdf

Downloads:

Package source:	DNNSIM_0.1.1.tar.gz
Windows binaries:	r-devel: DNNSIM_0.1.1.zip, r-release: DNNSIM_0.1.1.zip, r-oldrel: DNNSIM_0.1.1.zip
macOS binaries:	r-release (arm64): DNNSIM_0.1.1.tgz, r-oldrel (arm64): DNNSIM_0.1.1.tgz, r-release (x86_64): DNNSIM_0.1.1.tgz, r-oldrel (x86_64): DNNSIM_0.1.1.tgz

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